Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/5906
Title: | Feedback learning particle swarm optimization |
Authors: | Tang, Y Wang, Z Fang, J |
Keywords: | Particle swarm optimization;Feedback learning;Neural networks;Parameters estimation |
Issue Date: | 2011 |
Publisher: | Elsevier |
Citation: | Applied Soft Computing, 11(8): 4713 - 4725, Dec 2011 |
Abstract: | In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle’s history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail. |
Description: | This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published and is available at the link below - Copyright @ Elsevier 2011 |
URI: | http://bura.brunel.ac.uk/handle/2438/5906 |
DOI: | http://dx.doi.org/10.1016/j.asoc.2011.07.012 |
ISSN: | 1568-4946 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 486.74 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.